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Galit Agmon; Sameer Pradhan; Sharon Ash; Naomi Nevler; Mark Liberman; Murray Grossman; Sunghye Cho – Journal of Speech, Language, and Hearing Research, 2024
Purpose: Multiple methods have been suggested for quantifying syntactic complexity in speech. We compared eight automated syntactic complexity metrics to determine which best captured verified syntactic differences between old and young adults. Method: We used natural speech samples produced in a picture description task by younger (n = 76, ages…
Descriptors: Young Adults, Older Adults, Undergraduate Students, Caregivers
Lena Schmidt; Saleh Mohamed; Nick Meader; Jaume Bacardit; Dawn Craig – Research Synthesis Methods, 2024
The amount of grey literature and 'softer' intelligence from social media or websites is vast. Given the long lead-times of producing high-quality peer-reviewed health information, this is causing a demand for new ways to provide prompt input for secondary research. To our knowledge, this is the first review of automated data extraction methods or…
Descriptors: Automation, Natural Language Processing, Literature Reviews, Data Collection
Dazhen Tong; Yang Tao; Kangkang Zhang; Xinxin Dong; Yangyang Hu; Sudong Pan; Qiaoyi Liu – Asia Pacific Education Review, 2024
Artificial intelligence (AI) technologies have been consistently influencing the progress of education for an extended period, with its impact becoming more significant especially after the launch of ChatGPT-3.5 at the end of November 2022. In the field of physics education, recent research regarding the performance of ChatGPT-3.5 in solving…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Performance
Steven A. Stolz; Ali Lucas Winterburn; Edward Palmer – Educational Philosophy and Theory, 2024
The recent proliferation of Large Language Models (LLMs) raises questions as to the role of such tools both within an educational learning environment and their epistemic capacity. If, as Alfred North Whitehead remarked, western philosophy indeed 'consists of a series of footnotes to Plato', it would be of doubtless importance to evaluate the…
Descriptors: Artificial Intelligence, Technology Uses in Education, Natural Language Processing, Philosophy
Kanwal Zahoor; Narmeen Zakaria Bawany – Interactive Learning Environments, 2024
Mobile application developers rely largely on user reviews for identifying issues in mobile applications and meeting the users' expectations. User reviews are unstructured, unorganized and very informal. Identifying and classifying issues by extracting required information from reviews is difficult due to a large number of reviews. To automate the…
Descriptors: Artificial Intelligence, Computer Oriented Programs, Courseware, Learning Processes
Nikki Robertson-Griffin – Knowledge Quest, 2024
Schools (and some librarians) relegated librarians to the "business of books" and disregarded them of proven technological expertise. Fortunately, school librarians have either continued to insert themselves into digital technologies or emerged from the stacks to reclaim their role as digital resource experts in their schools. So, how do…
Descriptors: School Libraries, Librarians, Social Media, Technological Literacy
Jiaqi Yin; Tiong-Thye Goh; Yi Hu – International Journal of Educational Technology in Higher Education, 2024
Educational chatbots (EC) have shown their promise in providing instructional support. However, limited studies directly explored the impact of EC on learners' emotional responses. This study investigated the induced emotions from interacting with micro-learning EC and how they impact learning motivation. In this context, the EC interactions…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Psychological Patterns
Paiheng Xu; Jing Liu; Nathan Jones; Julie Cohen; Wei Ai – Annenberg Institute for School Reform at Brown University, 2024
Assessing instruction quality is a fundamental component of any improvement efforts in the education system. However, traditional manual assessments are expensive, subjective, and heavily dependent on observers' expertise and idiosyncratic factors, preventing teachers from getting timely and frequent feedback. Different from prior research that…
Descriptors: Educational Quality, Educational Assessment, Teacher Effectiveness, Natural Language Processing
Rebeckah K. Fussell; Emily M. Stump; N. G. Holmes – Physical Review Physics Education Research, 2024
Physics education researchers are interested in using the tools of machine learning and natural language processing to make quantitative claims from natural language and text data, such as open-ended responses to survey questions. The aspiration is that this form of machine coding may be more efficient and consistent than human coding, allowing…
Descriptors: Physics, Educational Researchers, Artificial Intelligence, Natural Language Processing
Michelle Pauley Murphy; Woei Hung – TechTrends: Linking Research and Practice to Improve Learning, 2024
Constructing a consensus problem space from extensive qualitative data for an ill-structured real-life problem and expressing the result to a broader audience is challenging. To effectively communicate a complex problem space, visualization of that problem space must elucidate inter-causal relationships among the problem variables. In this…
Descriptors: Information Retrieval, Data Analysis, Pattern Recognition, Artificial Intelligence
Maira Klyshbekova; Pamela Abbott – Electronic Journal of e-Learning, 2024
There is a current debate about the extent to which ChatGPT, a natural language AI chatbot, can disrupt processes in higher education settings. The chatbot is capable of not only answering queries in a human-like way within seconds but can also provide long tracts of texts which can be in the form of essays, emails, and coding. In this study, in…
Descriptors: Artificial Intelligence, Higher Education, Technology Uses in Education, Evaluation Methods
Jaeho Jeon; Seongyong Lee; Seongyune Choi – Interactive Learning Environments, 2024
Chatbot research has received growing attention due to the rapid diversification of chatbot technology, as demonstrated by the emergence of large language models (LLMs) and their integration with automatic speech recognition. However, among various chatbot types, speech-recognition chatbots have received limited attention in relevant research…
Descriptors: Literature Reviews, Content Analysis, Second Language Learning, Artificial Intelligence
Soomaiya Hamid; Narmeen Zakaria Bawany – Interactive Learning Environments, 2024
E-learning is the process of sharing knowledge out of the traditional classrooms through different online tools using internet. The availability and use of these tools are not easy for every student. Many institutions gather e-learning feedback to know the problems of students to improve their systems. In e-learning systems, typically a high…
Descriptors: Feedback (Response), Electronic Learning, Automation, Classification
Steffen Steinert; Karina E. Avila; Stefan Ruzika; Jochen Kuhn; Stefan Küchemann – Smart Learning Environments, 2024
Effectively supporting students in mastering all facets of self-regulated learning is a central aim of teachers and educational researchers. Prior research could demonstrate that formative feedback is an effective way to support students during self-regulated learning. In this light, we propose the application of Large Language Models (LLMs) to…
Descriptors: Formative Evaluation, Feedback (Response), Natural Language Processing, Artificial Intelligence
Kudzayi Savious Tarisayi – Cogent Education, 2024
As artificial intelligence proliferates, so do associated hopes and fears. This study explores such tensions within South African higher education following ChatGPT's launch, analyzing perceived threats alongside opportunities for responsibly harnessing benefits. Adopting a socio-technical framework recognizing technology's interdependence with…
Descriptors: Foreign Countries, Artificial Intelligence, Natural Language Processing, Technology Uses in Education